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Related Experiment Videos

Analyzing Raman maps of pharmaceutical products by sample-sample two-dimensional correlation.

Slobodan Sasić1, Donald A Clark, John C Mitchell

  • 1Pfizer Global Research and Development, Ramsgate Road, Sandwich, CT13 9NJ, UK.

Applied Spectroscopy
|June 23, 2005
PubMed
Summary

Sample-sample (SS) two-dimensional (2D) correlation spectroscopy effectively identifies unique spectral features in pharmaceutical samples. This method reliably generates chemical images for analyzing complex mixtures, offering a simple yet powerful approach.

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Chemometrics

Background:

  • Pharmaceutical analysis often requires detailed chemical imaging of complex mixtures.
  • Traditional spectral analysis can be challenging due to overlapping signals.
  • Advanced spectroscopic techniques are needed for accurate component identification.

Purpose of the Study:

  • To apply sample-sample (SS) two-dimensional (2D) correlation spectroscopy as a spectral selection tool.
  • To generate chemical images of pharmaceutical samples with two, three, and four components.
  • To compare the performance of SS 2D correlation with principal component analysis and orthogonal projection approach.

Main Methods:

  • Utilized SS 2D correlation spectroscopy on Raman mapping data.

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  • Analyzed the covariance matrix to identify unique spectra.
  • Generated chemical images by selecting least overlapped wavenumbers.
  • Compared SS 2D correlation with principal component analysis and OPA.

    • Main Results:

    • SS 2D correlation successfully identified unique spectra from the covariance matrix.
    • Chemical images were produced by univariate selection of wavenumbers.
    • The method demonstrated satisfactory performance compared to PCA and OPA.
    • SS 2D correlation effectively highlighted spectral differences and determined species presence.

    Conclusions:

    • SS 2D correlation is a simple and effective routine for producing reliable chemical images of unknown pharmaceutical samples.
    • The technique offers a straightforward approach based on minimal data processing commands.
    • Results closely correlate with the chemical features of the analyzed systems.